4 papers · 1 filter
Incorporating Domain Knowledge into Materials Tokenization
Yerim Oh, Jun-Hyung Park, Junho Kim +2
While language models are increasingly utilized in materials science, typical models rely on frequency-centric tokenization methods originally developed for natural language proces…
MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science
Junho Kim, Yeachan Kim, Jun-Hyung Park +3
We introduce a novel continued pre-training method, MELT (MatEriaLs-aware continued pre-Training), specifically designed to efficiently adapt the pre-trained language models (PLMs)…
Mentor-KD: Making Small Language Models Better Multi-step Reasoners
Hojae Lee, Junho Kim, SangKeun Lee
Large Language Models (LLMs) have displayed remarkable performances across various complex tasks by leveraging Chain-of-Thought (CoT) prompting. Recently, studies have proposed a K…
Efficient Pre-training of Masked Language Model via Concept-based Curriculum Masking
Mingyu Lee, Jun-Hyung Park, Junho Kim +2
Masked language modeling (MLM) has been widely used for pre-training effective bidirectional representations, but incurs substantial training costs. In this paper, we propose a nov…